arXiv:2606.13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales events present unique challenges for pricing including volatile demand patterns, rapid pricing decisions, and the need to balance short-term revenue with long-term profitability. We describe our approach combining daily-resolution demand forecasting using gradient-boosted trees with a multi-objective optimization framework that maximizes both long-term profit and net merc
The proliferation of AI forecasting techniques and powerful optimization algorithms are enabling more sophisticated and autonomous pricing strategies, spurred by competitive e-commerce markets.
This development allows e-commerce businesses to dynamically manage pricing at high frequency, significantly impacting revenue, profitability, and market competitiveness in sales campaigns.
Retail pricing strategies are evolving from static or periodic adjustments to real-time, AI-driven optimization, collapsing the time and human effort previously required for complex sales decisions.
- · E-commerce platforms
- · Fashion retail brands
- · AI software providers
- · Consumers (potentially, via competitive pricing)
- · Traditional retail (slow to adapt)
- · Small businesses (lacking AI investment)
- · Manual pricing analysts
Increased revenue and profit margins for e-commerce retailers adopting advanced AI pricing.
Heightened competition in e-commerce as dynamic pricing becomes standard, leading to potential price wars and market consolidation.
The development of counter-algorithms by consumers or regulatory bodies to detect and respond to potentially predatory or unfair high-frequency pricing.
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Read at arXiv cs.LG